Graphical Causal Models

Abstract

This chapter gives an introduction to causal modeling, in particular to causal Bayesian networks. It starts by introducing causal models and their importance. Then causal Bayesian networks are described, including two types of causal reasoning, prediction and counterfactuals. It continues with the topic of learning causal models, presenting one of the state-of-the-art techniques. Finally, it shows an example of learning causal models from real-world data about children with Attention Deficit Hyperactivity Disorder.